Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction.

Journal: BMC cardiovascular disorders
PMID: 40355836

Abstract

BACKGROUND: Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing treatment strategies. This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data.

Authors

  • Chenglong Guo
    Pulmonary Vascular Disease Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Binyu Gao
    Biological Science & Medical Engineering, Southeast University, Nanjing, 518000, China.
  • Xuexue Han
    Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
  • Tianxing Zhang
    Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
  • Tianqi Tao
    Department of Geriatrics, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China. ttqtxt@163.com.
  • Jinggang Xia
    Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China. xiajinggang@sina.cn.
  • Honglei Liu
    School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China. liuhonglei@ccmu.edu.cn.

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